This book deals with dynamic and stochastic methods for multi-project planning. Based on the idea of using queueing networks for the analysis of dynamic-stochastic multi-project environments this book addresses two problems: detailed scheduling of project activities, and integrated order acceptance and capacity planning. In an extensive simulation study, the book thoroughly investigates existing scheduling policies. To obtain optimal and near optimal scheduling policies new models and algorithms are proposed based on the theory of Markov decision processes and Approximate Dynamic programming. Then the book presents a new model for the effective computation of optimal policies based on a Markov decision process. Finally, the book provides insights into the structure of optimal policies.

Project planning, scheduling, and control are regularly used in business and the service sector of an economy to accomplish outcomes with limited resources under critical time constraints. To aid in solving these problems, network-based planning methods have been developed that now exist in a wide variety of forms, cf. Elmaghraby (1977) and Moder et al. (1983). The so-called "classical" project networks, which are used in the network techniques CPM and PERT and which represent acyclic weighted directed graphs, are able to describe only projects whose evolution in time is uniquely specified in advance. Here every event of the project is realized exactly once during a single project execution and it is not possible to return to activities previously carried out (that is, no feedback is permitted). Many practical projects, however, do not meet those conditions. Consider, for example, a production process where some parts produced by a machine may be poorly manufactured. If an inspection shows that a part does not conform to certain specifications, it must be repaired or replaced by a new item. This means that we have to return to a preceding stage of the production process. In other words, there is feedback. Note that the result of the inspection is that a certain percentage of the parts tested do not conform. That is, there is a positive probability (strictly less than 1) that any part is defective.

This book aims to include the effects of a progressive personal tax into the deterministic dynamic theory of the firm. To this end the author investigates the impact of a progressive personal tax on the optimal dividend, financing and investment policy of a shareholder-controlled, value-maximising firm. More specifically, the principal aim is the justification of the thesis that during each stage of their evolution, firms will be controlled by investors in different tax brackets. With this aim in mind, the author develops a dynamic equilibrium and portfolio theory under certainty, which considers: - the market value of an arbitrary firm such that no excess demand for or supply of shares exists, - the portfolio selection of differently taxed investors, - the succession of differently taxed investors, who possess the shares of any value-maximizing firm, in the course of time, - the optimal resulting policy string and corresponding evolution of a firm in the course of time.

Improvements in the performance of a freight transport system can be achieved either through technological innovation or by using advanced planning tools. This volume includes contributions on planning which cover the following topics: - analysis of current trends in developed countries, - demand analysis and forecasting, - flows simulation and prediction, - shipment and delivery problems, - regulation problems, - investment evaluation. Papers consider such applications as warehouse location, crude oil transportation, newspaper distribution, the trucking industry, rail planning and seaport systems. Transport issues in North America and Italy are described and compared. The papers in this volume are revised versions of contributions to the International Seminar on Freight Transport Planning and Logistics held in Bressanone, Italy, in July 1987.

This monograph is intended for an advanced undergraduate or graduate course of engineering and management science. as well as for persons in business. industry. military or in any field. who want an introductory and a capsule look into the methods of group decision making under multiple criteria. This is a sequel to our previous works entitled "Multiple Objective Decision Making--Methods and Applications (No. 164 of the Lecture Notes). and "Multiple Attribute Decision Making--Methods and Applications (No. 186 of the Lecture Notes). Moving from a single decision maker (the consideration of Lecture Notes 164 and 186) to a multiple decision maker setting introduces a great deal of complexity into the analysis. The problem is no longer the selection of the most preferred alternative among the nondominated solutions according to one individual's (single decision maker's) preference structure. The analysis is extended to account for the conflicts among different interest groups who have different objectives. goals. and so forth. Group decision making under multiple criteria includes such diverse and interconnected fields as preference analysis. utility theory. social choice theory. committee decision theory. theory of voting. game theory. expert evaluation analysis. aggregation of qualitative factors. economic equilibrium theory. etc; these are simplified and systematically classified for beginners. This work is to provide readers with a capsule look into the existing methods. their characteristics. and applicability in the complexity of group decision making.

Approaches to project scheduling under resource constraints are discussed in this book. After an overview of different models, it deals with exact and heuristic scheduling algorithms. The focus is on the development of new algorithms. Computational experiments demonstrate the efficiency of the new heuristics. Finally, it is shown how the models and methods discussed here can be applied to projects in research and development as well as market research.

This volume contains contributions from the 11th International Conference on Management Science (CMS 2014), held at Lisbon, Portugal, on May 29-31, 2014. Its contents reflect the wide scope of Management Science, covering different theoretical aspects for a quite diverse set of applications. Computational Management Science provides a unique perspective in relevant decision-making processes by focusing on all its computational aspects. These include computational economics, finance and statistics; energy; scheduling; supply chains; design, analysis and applications of optimization algorithms; deterministic, dynamic, stochastic, robust and combinatorial optimization models; solution algorithms, learning and forecasting such as neural networks and genetic algorithms; models and tools of knowledge acquisition, such as data mining; and all other topics in management science with the emphasis on computational paradigms.